-
securityF
license-
qualityIntegrates with the AgentCraft framework to enable secure communication and data exchange between AI agents, supporting both premade and custom enterprise AI agents.
Last updated -
1
Offers community support through a Discord server for real-time help and discussions
Offers containerized deployment of the ToolFront MCP server through Docker images
Provides connection to DuckDB databases, allowing AI agents to execute queries and explore DuckDB data
Provides issue tracking and repository hosting for the ToolFront project
Enables GitHub Copilot to connect to your databases, providing context about tables, schemas, and query patterns
Enables connections to MySQL databases, allowing AI agents to query and analyze MySQL data
Provides connection to PostgreSQL databases, allowing AI agents to query and work with PostgreSQL data
Enables connections to Snowflake data warehouses, allowing AI agents to query and analyze Snowflake data
Provides connection to SQLite databases, allowing AI agents to query and analyze SQLite data
Data environments for AI agents
Documentation:
Source code:
Installation
Install toolfront with pip or your favorite PyPI package manager.
pip install toolfront
Quickstart
ToolFront helps you build and deploy environments for AI agents. Think of environments as interactive directories that agents can explore and take actions in.
environment
├── index.md
├── page/
│ ├── cli.py
│ └── index.md
└── data/
├── sample.txt
└── data.csv
To add actions to an environment, simply define commands in any markdown header. As agents browse files, they will discover these tools and learn how to use them with the --help flag.
---
tools:
- [python3, cli.py]
- [curl, -X, GET, https://api.example.com/data]
---
# My environment page
Add [links](./page_1) to tell your agents what pages they should check out.
Agents can call any command defined in markdown headers.
- `python3 cli.py` executes a python script
- `curl -X GET https://api.example.com/data` calls an API
You can launch browsing sessions with ToolFront's Python SDK, or build your own browsing agent with the MCP. Browsing is always powered by your own models.
Using the SDK
from toolfront import Browser
browser = Browser(model="openai:gpt-5")
url = "file:///path/to/environment"
answer = browser.ask("What's our average ticket price?", url=url)
print(answer)
Using MCP
{
"mcpServers": {
"toolfront": {
"command": "uvx",
"args": ["toolfront", "mcp", "file:///path/to/toolsite"]
}
}
}
ToolFront comes with six core tools* your agents can use to interact with environments:
run_command- Execute commands defined in markdown headersread- Get the content of a specific page or fileglob- List files matching a patterntree- View directory structuregrep* - Find relevant lines that match a regular expressionsearch* - Find relevant documents containing a list of terms
* grep
ToolFront Cloud
Instantly deploy your environments with ToolFront Cloud.
toolfront deploy ./path/to/environment --api-key "my-api-key"
Would give you a secure environment URL your agents can browse.
from toolfront import Browser
browser = Browser(params={"TOOLFRONT_API_KEY": "my-api-key"})
answer = browser.ask(..., url="https://cloud.toolfront.ai/user/environment")
Environments deployed to ToolFront Cloud are automatically indexed and get access to grep and search tools.
ToolFront Cloud is currently in open beta. To request access, join our Discord or email esteban[at]kruskal[dot]ai.
Community & Contributing
Discord: Join our community server for real-time help and discussions
X: Follow us @toolfront for updates and news
Issues: Report bugs or request features on GitHub Issues
License
This project is licensed under the terms of the MIT license.
This server cannot be installed
local-only server
The server can only run on the client's local machine because it depends on local resources.
Securely connects AI agents to multiple databases simultaneously while enabling collaborative learning from team query patterns, all while keeping data private by running locally.
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